Field
Embodiments included herein generally relate to parallel or concurrent decoding. More particularly, embodiments relate to combining results from multiple decoders to find an accurate decoded solution.
Background
The performance of data pattern recognition systems such as, for example, speech recognition systems can be improved by operating multiple recognition engines in parallel or concurrently and then combining the results from each of the multiple recognition engines. These improvements can be attributed to optimizations implemented in each of the multiple recognition engines for a particular task or condition (e.g., a portion of a speech recognition process) such that the combination of the results from each of the recognition engines can provide a solution for the data pattern recognition process. However, there are at least two drawbacks with this type of recognition system.
First, power consumption increases proportionately with the number of recognition engines. This can degrade battery lifetime in devices, especially mobile devices, implementing the recognition system with multiple recognition engines. Second, the combination of the results from each of the recognition engines may not provide a solution with equal or greater accuracy as compared to a solution provided by a single recognition engine. Thus, the recognition system not only provides an inaccurate solution based on the results from the multiple recognition engines, but also provides the inaccurate solution at the expense of additional power consumption.